{"id":"W4391666339","doi":"10.2208/jscejj.23-23187","title":"COMPARATIVE ANALYSIS OF MARKOV CHAIN MODEL ESTIMATION METHODS BASED ON VISUAL INSPECTION DATA","year":2023,"lang":"en","type":"article","venue":"Japanese Journal of JSCE","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"L'Alliance Boviteq","funders":"","keywords":"Markov chain; Computer science; Visual inspection; Markov chain Monte Carlo; Markov model; Artificial intelligence; Pattern recognition (psychology); Machine learning; Bayesian probability","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020522,0.0009949205,0.001446154,0.003641246,0.0007348746,0.001664464,0.001413728,0.001550483,0.002449035],"category_scores_gemma":[0.09351072,0.0004768173,0.001536034,0.002031565,0.000686736,0.00326019,0.0009737372,0.001409116,0.0003489239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001629197,"about_ca_system_score_gemma":0.002224378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01255953,"about_ca_topic_score_gemma":0.006910969,"domain_scores_codex":[0.9927709,0.005141441,0.0003223929,0.0006131867,0.0009166059,0.0002355162],"domain_scores_gemma":[0.7806779,0.2054561,0.002848631,0.002997401,0.007471043,0.0005489973],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002733298,0.0004120904,0.02712706,0.0009608961,0.000900311,0.000217605,0.0009111344,0.6155918,0.002468281,0.02131604,0.002444389,0.3249171],"study_design_scores_gemma":[0.0000410805,0.0001339511,0.004658317,0.00005945675,0.0001075311,0.00006225662,0.000159402,0.9893997,0.0008873933,0.004118425,0.0003296059,0.00004285957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2092741,0.002762265,0.7824228,0.0004076404,0.0001331253,0.0001694883,0.0002754065,0.001316179,0.003238916],"genre_scores_gemma":[0.8180447,0.0009952326,0.1784389,0.00008246728,0.00005080594,0.0002297437,0.0008901533,0.0002854066,0.0009825483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.020522,"threshold_uncertainty_score":0.108532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3236059678460392,"score_gpt":0.5795618829135618,"score_spread":0.2559559150675226,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}